CODEWITHSUNDEEP
pythonscikit-learnrandom-forest
Sullivan Tan
Sullivan Tan

Reputation: 1

How to read the fitting result of Random Forest ? sklearn

I mean the result, not the theory:

In linear regression, there is a formula to explain the variables and weights that contribute the final score. In decision tree, there is a path map to explain what conditions result in the segmentation.

The only result I can read from < from sklearn.tree import DecisionTreeRegressor> is by pickle.dump. But pickle is still a black-box. Although features_importance_ output explains the weight importance of each features, however, that's an indirect method. I still cannot understand how the score come from.

How read the data and explain the fitting result of Random Forest directly? Is there any formula or path map?

Upvotes: 0

Views: 141

Answers (1)

Nicolas Gervais
Nicolas Gervais

Reputation: 36624

With sklearn.tree.export_graphviz and dot you can visualize the decision making process. It's a little tricky to implement but that's a way to read the fitting result. Read more here.

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Upvotes: 1

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